FlowPure: Continuous Normalizing Flows for Adversarial Purification

1. 1Implement continuous normalizing flows to model the distribution of adversarial examples
2. 2Use the forward process to inject Gaussian noise and dilute adversarial perturbations
3. 3Apply the reverse process to purify the input data and improve model robustness
4. 4Evaluate the effectiveness of FlowPure in removing adversarial perturbations and improving model accuracy

ML researchers and engineers working on adversarial robustness can benefit from this approach to improve model security and reliability

💡 Continuous normalizing flows can be used to effectively remove adversarial perturbations and improve model robustness

🚫 Improve model robustness with FlowPure, a new approach to adversarial purification using continuous normalizing flows!